Releases: incognite-lab/myGym
Releases · incognite-lab/myGym
Release list
myGym 4.0
Changelog:
- new humanoids - Unitree G1
- new unittests
- new scripts for better test of robots and tasks
- updates in oraculum
- robot repositioned to the same coordinate system
- rbot urdf updates
- IK solver update
- robot control update with preparation to body parts control
- preparation for PRAG
myGym 3.10
myGym 3.10
changelog:
Stable Baselines 3
Gymnasium
new multippo implementation
multiprocessing on any number of CPU
Nico robot and envs
weight visualization
training visualization with subgoal analysis
oraculum method to test novel tasks
protorewards
atomic actions
v3.7
myGym 3.7
changelog:
new robots (Nico, Tiago, HSR)
new workspaces (human collaborative, Tiago table, Nico table)
new algorithms for multi-step training
visualization of multiple tranings in one graph
sim2real for Nico robot
new compositional rewards
myGym 2.2.1
Changelog:
- fixed IK bug with end effector and gripper navigation
- fixed bug with observation space
- languade module WIP
- new humanoid robot Thiago
- new algoritms for multi-policy - MultiACKTR
myGym 2.1.2
CHANGELOG:
- new visualization of training progress for multiple runs with variability - visaversucces.py
- fixed bugs with multi step rewarding
- new multi network algorithm - multiPPO2
- new task with diverse subgoals - Pick and rotate
myGym 2.1
Changelog:
- new URDF with robot automatic robot gripper annotation
- new robot upload and gripper control
- magnetic and mechanic gripper operation
- new tasks for multi step manipulation
- keyboard control of robots in test.py
- new observation definition
- robot force and speed control in config
myGym 2.0
Changelog:
- Tested support for Tensorflow 2.0
- Success training of PNP tasks
- Support for multi-step tasks
- Ability to train multiple networks within one tasks
myGym 1.5
Changelog:
- Faster implemetation of the training algorithms
- Novel tasks - press,turn, switch
- Better evaluation of the results
- Novel reward functions
- Template for own implementation of NN